GC-511 Predicting Stock Prices Using Different Machine Learning and Deep Learning Models

Our project focuses on the challenge of predicting the daily closing prices and stock movements of Amazon, one of the world\u27s largest and most dynamic corporations. Amazon\u27s stock prices are known for their unpredictability and are influenced by a multitude of intricate factors. Our project aims to provide accurate and reliable forecasts for Amazon\u27s stock prices, going beyond mere predictions. The analysis employs a comprehensive approach, comparing the performance of three distinct machine learning and deep learning models: Linear Regression, Support Vector Machine (SVM), and Multi-Layered Perceptron (MLP) for financial time series data. The dataset we used spans from January 2, 2005, to August 21, 2019, covering a substantial period of Amazon\u27s stock history. Our project not only delivers precise predictions but also outlines the methodologies and techniques used for stock price forecasting